This research proposes a reliable model for identifying different construction materials with the highest accuracy, which is exploited as an advantageous tool for a wide range of construction applications such as automated progress monitoring. In this study, a novel deep learning architecture called Vision Transformer (ViT) is used for detecting and classifying construction materials. The robustness of the employed method is assessed by utilizing different image datasets. For this purpose, the model is trained and tested on two large imbalanced datasets, namely Construction Material Library (CML) and Building Material Dataset (BMD). A third dataset is also generated by combining CML and BMD to create a more imbalanced dataset and assess the capabilities of the utilized method. The achieved results reveal an accuracy of 100 percent in evaluation metrics such as accuracy, precision, recall rate, and f1-score for each material category of three different datasets. It is believed that the suggested model accomplishes a robust tool for detecting and classifying different material types. To date, a number of studies have attempted to automatically classify a variety of building materials, which still have some errors. This research will address the mentioned shortcoming and proposes a model to detect the material type with higher accuracy. The employed model is also capable of being generalized to different datasets.
翻译:这项研究提出了一种可靠的模型,用于确定具有最高准确度的不同建筑材料,该模型被作为自动化进度监测等各种建筑应用的有利工具加以利用。在本研究中,采用了称为View 变异器(VIT)的新型深层次学习结构来探测和分类建筑材料。使用不同的图像数据集来评估所用方法的稳健性。为此目的,该模型在建筑材料图书馆(CML)和建筑材料数据集(BMD)这两个大型的不平衡数据集中进行了培训和测试。第三个数据集也是通过将CML和BMD合并来创建更不平衡的数据集并评估所用方法的能力而生成的。取得的结果显示,评价指标的准确度达到100%,如准确度、精确度、回溯率和对三个不同数据集的每种材料类别的f1-核心。相信,所建议的模型能够实现一种强有力的工具,用于检测和分类不同的材料类别。迄今为止,一些研究试图对各种建筑材料进行自动分类,这些材料仍有一些错误。这一研究将解决上述短绌之处并评估所用方法的能力。所得出的研究结果表明,一种用于检测不同材料类型的标准是更高程度的模型。